Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0ad778fa57af47912dc7d5ae322d2b579de0765115d0bf8062c2a0381aa2123
- Size of remote file:
- 929 kB
- SHA256:
- d633a7f884c300d582e7e6c2b1c08e3ecf0eacae147965f232e84858a9e9680f
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